Imbalanced Data Classification Based on Feature Selection Techniques
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The difficulty of the many classification tasks lies in the analyzed data nature, as disproportionate number of examples from different class in a learning set. Ignoring this characteristics causes that canonical classifiers display strongly biased performance on imbalanced datasets. In this work a novel classifier ensemble forming technique for imbalanced datasets is presented. On the one hand it takes into consideration selected features used for training individual classifiers, on the other hand it ensures an appropriate diversity of a classifier ensemble. The proposed method was tested on the basis of the computer experiments carried out on the several benchmark datasets. Their results seem to confirm the usefulness of the proposed concept.
KeywordsMachine learning Classification Imbalanced data Feature selection Random search
This work was supported by the Polish National Science Center under the grant no. UMO-2015/19/B/ST6/01597 as well as Statutory Found of the Faculty of Electronics, Wroclaw University of Science and Technology.
- 1.Ahmed, F., Samorani, M., Bellinger, C., Zaïane, O.R.: Advantage of integration in big data: feature generation in multi-relational databases for imbalanced learning. In: 2016 IEEE International Conference on Big Data, BigData 2016, Washington DC, USA, 5–8 December 2016, pp. 532–539 (2016)Google Scholar
- 2.Alcalá-Fdez, J., Fernández, A., Luengo, J., Derrac, J., García, S., Sánchez, L., Herrera, F.: Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J. Multiple-Valued Logic Soft Comput. 17 (2011)Google Scholar
- 10.Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
- 12.Triguero, I., Galar, M., Merino, D., Maillo, J., Bustince, H., Herrera, F.: Evolutionary undersampling for extremely imbalanced big data classification under apache spark. In: IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, BC, Canada, 24–29 July 2016, pp. 640–647 (2016)Google Scholar